Intellignos - WebCongress Mexico 2016

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Information Insights Action Machine Learning Predictive analytics en esteroides INFORMATION, INSIGHTS & ACTION

Transcript of Intellignos - WebCongress Mexico 2016

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Information Insights Action

Machine Learning Predictive analytics en esteroides

INFORMATION, INSIGHTS & ACTION

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Mission

We help the most challenging companies in the world improve their competitive situation through the use of Big Data in new an creative ways.

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Some of our clients

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Credentials

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Aplicacióndealgoritmos(mecanismoscondicionales)aseriesdedatosconelobje8vodegenerarconocimiento.

¿QUÉESMACHINELEARNING?

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¿QUÉESUNALGORITMO?

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APLICACIONESDEMACHINELEARNINGDeteccióndeRostro

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APLICACIONESDEMACHINELEARNINGReconocimientodeRostro

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APLICACIONESDEMACHINELEARNINGReconocimientodeImagen

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APLICACIONESDEMACHINELEARNINGReconocimientodeVoz

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APLICACIONESDEMACHINELEARNINGReconocimientodepatrónesgenéGcos

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APLICACIONESDEMACHINELEARNINGReconocimientodeFraudes

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APLICACIONESDEMACHINELEARNINGIdenGficarposiblereaccióndeunsegmentodeusuariosa

ciertosaccionesdemarkeGng(EmailMktg,FacebookAd,etc)

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APLICACIONESDEMACHINELEARNINGDetecciónderetrasosdevuelos

(ypotencialPublicidad/Marke8ng)

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APLICACIONESDEMACHINELEARNINGPredeciraccionesparaganarunaelección

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APLICACIONESDEMACHINELEARNINGPredecirgustos(Películas,Música,Productos,Servicios)

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APLICACIONESDEMACHINELEARNINGPredecirPublicidaddeseada

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APLICACIONESDEMACHINELEARNINGPredecirLandingpagesdinámicassegúngustos

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MACHINELEARNINGSUPERVISADOYNOSUPERVISADO(CLUSTERING)

SUPERVISADO=PREDICTIVO NOSUPERVISADO=EXPLORATORIO

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MACHINELEARNINGEJEMPLODENOSUPERVISADO(CLUSTERING)

Código Película NiveldeLenguajeInapropiado NiveldeExposicióndelcuerpo169 NOAPTA 3.58 1290170 NOAPTA 3.58 1295171 NOAPTA 3.59 1035172 NOAPTA 3.63 1015173 APTA 3.64 380174 APTA 3.69 465175 NOAPTA 3.71 780176 NOAPTA 3.82 845177 NOAPTA 3.92 1065178 NOAPTA 4 1035

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MACHINELEARNINGEJEMPLODENOSUPERVISADO(CLUSTERING)

LenguajeInapropiado

Niveldeexposicióndelcuerpo

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MACHINELEARNINGEJEMPLODENOSUPERVISADO(CLUSTERING)

LenguajeInapropiado

Niveldeexposicióndelcuerpo

Unaformaesdeterminarladensidadosealadistanciaentrelosdiferentespuntos.

Bajadensidad

Altadensidad

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MACHINELEARNINGEJEMPLODENOSUPERVISADO(CLUSTERING)

LenguajeInapropiado

Niveldeexposicióndelcuerpo

Euclideandistance

Ladensidadpuedeserdeterminadaconvariosmétodos

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MACHINELEARNINGEJEMPLODENOSUPERVISADO(CLUSTERING)

LenguajeInapropiado

Niveldeexposicióndelcuerpo

ManhaXanDistance

Ladensidadpuedeserdeterminadaconvariosmétodos

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MACHINELEARNINGEJEMPLODENOSUPERVISADO(CLUSTERING)

LenguajeInapropiado

Niveldeexposicióndelcuerpo

Antesdenormalizar Despuésdenormalizar

LenguajeInapropiado

Niveldeexposicióndelcuerpo

SinnormalizaciónelNiveldeExposicióndeCuerpovaapesarmásqueLenguajeporquesusunidadessemidenencientosymiles.

Lainformaciónnormalizadaesimportanteparaalgunosalgoritmosparametricos.

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MACHINELEARNINGEJEMPLODENOSUPERVISADO(CLUSTERING)

AlgoritmoK-Means

1.  Seleccionarunnúmerodeclusters.

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MACHINELEARNINGEJEMPLODENOSUPERVISADO(CLUSTERING)

AlgoritmoK-Means

1.  Seleccionarunnúmerodeclusters.2.  Asignar(aleatoriamente)unpuntode

inicioparael“centroide”decadacluster.

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MACHINELEARNINGEJEMPLODENOSUPERVISADO(CLUSTERING)

AlgoritmoK-Means

1.  Seleccionarunnúmerodeclusters.2.  Asignar(aleatoriamente)unpuntode

inicioparael“centroide”decadacluster.3.  Asignarcadapuntoaunclusterbasadoen

elcentroide.

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MACHINELEARNINGEJEMPLODENOSUPERVISADO(CLUSTERING)

AlgoritmoK-Means

1.  Seleccionarunnúmerodeclusters.2.  Asignar(aleatoriamente)unpuntode

inicioparael“centroide”decadacluster.3.  Asignarcadapuntoaunclusterbasadoen

elcentroide.4.  Muevaloscentroidesalcentro

geométricodelospuntosqueselesasigna.

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MACHINELEARNINGEJEMPLODENOSUPERVISADO(CLUSTERING)

AlgoritmoK-Means

1.  Seleccionarunnúmerodeclusters.2.  Asignar(aleatoriamente)unpuntode

inicioparael“centroide”decadacluster.3.  Asignarcadapuntoaunclusterbasadoen

elcentroide.4.  Muevaloscentroidesalcentro

geométricodelospuntosqueselesasigna.

5.  Repe8r3.

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MACHINELEARNINGEJEMPLODENOSUPERVISADO(CLUSTERING)

AlgoritmoK-Means

1.  Seleccionarunnúmerodeclusters.2.  Asignar(aleatoriamente)unpuntode

inicioparael“centroide”decadacluster.3.  Asignarcadapuntoaunclusterbasadoen

elcentroide.4.  Muevaloscentroidesalcentro

geométricodelospuntosqueselesasigna.

5.  Repe8r3.6.  Repe8r4.

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MACHINELEARNINGEJEMPLODENOSUPERVISADO(CLUSTERING)

AlgoritmoK-Means

1.  Seleccionarunnúmerodeclusters.2.  Asignar(aleatoriamente)unpuntode

inicioparael“centroide”decadacluster.3.  Asignarcadapuntoaunclusterbasadoen

elcentroide.4.  Muevaloscentroidesalcentro

geométricodelospuntosqueselesasigna.

5.  Repe8r3.6.  Repe8r4.7.  Repe8rnuevamente.

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MACHINELEARNINGEJEMPLODENOSUPERVISADO(CLUSTERING)

AlgoritmoK-Means

1.  Seleccionarunnúmerodeclusters.2.  Asignar(aleatoriamente)unpuntode

inicioparael“centroide”decadacluster.3.  Asignarcadapuntoaunclusterbasadoen

elcentroide.4.  Muevaloscentroidesalcentro

geométricodelospuntosqueselesasigna.

5.  Repe8r3.6.  Repe8r4.7.  Repe8rnuevamente,hastallegaralpunto

deconvergencia.

APTAS

NOAPTAS

SINCLASIFICAR

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MACHINELEARNINGEJEMPLODENOSUPERVISADO(CLUSTERING)

Código Película NiveldeLenguajeInapropiado NiveldeExposicióndelcuerpo169 NOAPTA 3.58 1290170 NOAPTA 3.58 1295171 NOAPTA 3.59 1035172 NOAPTA 3.63 1015173 APTA 3.64 380174 APTA 3.69 465175 NOAPTA 3.71 780176 NOAPTA 3.82 845177 NOAPTA 3.92 1065178 NOAPTA 4 1035

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MACHINELEARNINGEJEMPLODENOSUPERVISADO(CLUSTERING)

AlgoritmoK-Means

1.  PorsupuestoquedependiendodelvalordeKelegido,elmodelodedependenciacambiatotalmente.

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MACHINELEARNINGEJEMPLODENOSUPERVISADO(CLUSTERING)

Otrosalgoritmosconlosmismosdatos

Algoritmosbasadosencentroides(comoK-Mean)

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MACHINELEARNINGEJEMPLODENOSUPERVISADO(CLUSTERING)

Otrosalgoritmosconlosmismosdatos

Algoritmosjerarquicos,queseiniciaporunióndeelmáscercanoolaseparacióndelospuntosmásalejados.

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MACHINELEARNINGEJEMPLODENOSUPERVISADO(CLUSTERING)

Otrosalgoritmosconlosmismosdatos

Neighborhoodgrowers.Inicianenzonasdealtadensidadycrecenhaciaafuera.

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MACHINELEARNINGEJEMPLODESUPERVISADO

Enaprendizajesupervisado,losdatoscomienzan(adiferenciadeNosupervisado)cone8quetasyfeaturesocaracterís8cas(consusrespec8vasmediciones).Elfoconoesexplora8vosinopredic8vo.AprendizajesupervisadoimplicasiempreINDUCCIÓN(delopar8cularalogeneral).

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MACHINELEARNINGMODELOSDESUPERVISADO

CLASIFICACIÓNVSREGRESIÓN

¿CUÁNDOUTILIZAMOSCUAL?

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MACHINELEARNINGSUPERVISADO-CLASIFICACIÓN

Clasificación:Basadoenseriesdedatosexistentesjuntoconsuscaracterís8casofeatures,unopodríaclasificarnuevosXenbaseaTrue/False(Discreto)

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MACHINELEARNINGSUPERVISADO-CLASIFICACIÓN

Decisiontrees(Clasificación):Partesdeárboldedecision.

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MACHINELEARNINGSUPERVISADO-CLASIFICACIÓN

Decisiontrees(Clasificación):LosformatospuedenserAlgoritmooRepresentación.

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MACHINELEARNINGSUPERVISADO-REGRESIÓN

Regresión:Basadoenunconjuntodepuntos(valores)podemoses8marelvalor(notrueorfalse)quetomaráYparaunnuevoX(Con8nuo)Ladifrenciasobreparaqueu8lizarcada8podemodeloestábasadaeneloutputynoenelinput.

1 2 4 6 8

1 4 16 36 64

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MACHINELEARNINGSUPERVISADO-REGRESIÓN

r=CoeficientedeCorrelaciónrcuadrado=Coeficientededeterminación

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MACHINELEARNINGSUPERVISADO-REGRESIÓN

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MACHINELEARNINGEJEMPLODESUPERVISADO

Definamosrespuestaquequisieramostenerennuestronegocio.

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MACHINELEARNINGEJEMPLODESUPERVISADO

Ahora,definamoscualesdeberíanserresueltasconmetodologíasdeclasificaciónycualesconmetodologíasderegresión.

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EJEMPLOPRACTICO–Modelodeatribución1.Situación.

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EJEMPLOPRACTICO–Modelodeatribución2.Problemaasolucionar:Quemediosrealmenteson

responsablesdelasventas?

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EJEMPLOPRACTICO–Modelodeatribución3.QueGpodealgoritmouGlizamos?

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EJEMPLOPRACTICO–Modelodeatribución3.QueGpodealgoritmouGlizamos?

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EJEMPLOPRACTICO–Modelodeatribución4.Aplicamoselalgoritmoacientosdemilesdevariablesde

campañasycanalesdetráficocambiandoasuvezlaposicióndelavisita.

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EJEMPLOPRACTICO–Modelodeatribución4.Aplicamoselalgoritmoacientosdemilesdevariablesde

campañasycanalesdetráficocambiandoasuvezlaposicióndelavisita.

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EJEMPLOPRACTICO–Modelodeatribución5.Obtenemos:-‘r’(coeficientedecorrelación)nosdicequetanresponsableeselmedioenlaconversióndependiendodelaposiciónenqueseuGlizóparallegaralsiGo.Sinohaycorrelaciónsedesechanlasseries.Estonosdaráelpesoquetendrácadaunadelasvariablesenelalgoritmo.

-rcuadrado:Ademáselcoeficientededeterminaciónnosdicequetantolasseriestomadasexplicanelmodelo.

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EJEMPLOPRACTICO–Modelodeatribución6.Searmaelmodelo(Algoritmo)

DayIndex e-CommerceTransactions OrganicSearch Promotional_EmailsDirect PaidSearch Referrals Display Social TOTALTRAFFIC Direct OrganicSearchPaidSearch Email Referral Display SocialR 0.90299 0.64549 0.93317 0.48279 0.624 0.30269 0.77176 0.9401 0.94307 0.94041 0.87578 0.87945 0.79135 0.93655R Square 0.81539 0.41665 0.8708 0.23309 0.38938 0.09162 0.59562 0.88379 0.88938 0.88437 0.76699 0.77344 0.62623 0.87713

LASTCLICK(Goalconversionspurchaseandtrafficperchannel) ASSISTED(Conversion-AssitedConversion)

8/1/14 51,573 1,496,869 25,600 1,128,677 514,167. 154,950 49,841 38,971 3,409,075 31,881 26,934 15,785 8,514 12,413 3,273 3,2148/2/14 32,011 1,317,800 18,140 962,035 440,350. 111,035 46,354 34,064 2,929,778 19,256 16,434 9,938 5,011 7,472 2,177 1,8778/3/14 29,966 1,374,188 15,067 973,741 456,352. 113,189 51,025 34,055 3,017,617 18,479 15,979 9,485 4,599 7,176 2,104 1,7358/4/14 68,205 1,761,214 59,998 1,374,295 240,912. 160,413 59,221 44,859 3,700,912 41,994 35,171 20,779 11,420 15,685 4,301 4,2788/5/14 66,449 1,736,971 30,994 1,302,472 238,305. 165,864 51,542 50,856 3,577,004 40,882 34,457 20,166 10,728 14,923 4,198 4,1478/6/14 66,880 1,716,113 25,004 1,304,721 236,732. 163,912 48,723 53,296 3,548,501 40,180 34,070 20,055 10,383 14,482 4,218 3,9448/7/14 65,534 1,682,089 28,286 1,268,615 234,037. 162,380 40,993 43,850 3,460,250 40,739 34,452 20,354 10,334 14,553 4,165 4,1308/8/14 56,090 1,516,054 33,783 1,161,337 204,935. 139,517 34,995 37,597 3,128,218 35,346 29,730 17,680 9,160 12,742 3,667 3,6288/9/14 30,636 1,230,277 18,644 916,635 186,160. 120,432 29,682 28,082 2,529,912 19,427 16,425 9,779 4,923 7,204 2,215 2,0088/10/14 23,568 1,236,132 16,708 907,235 173,708. 112,634 27,874 26,735 2,501,026 14,784 12,454 7,338 3,750 5,533 1,631 1,5308/11/14 56,350 1,649,074 55,059 1,342,990 246,385. 169,856 30,894 38,976 3,533,234 34,407 29,053 17,081 8,946 12,574 3,731 3,5298/12/14 53,147 1,646,438 51,176 1,247,347 261,767. 189,324 30,099 45,667 3,471,818 31,719 26,590 15,450 8,349 11,717 3,242 3,2288/13/14 51,856 1,562,445 54,363 1,198,780 257,274. 176,203 47,840 44,908 3,341,813 30,940 26,136 15,148 8,184 11,789 3,210 3,2038/14/14 47,618 1,511,492 45,357 1,171,641 205,615. 138,591 33,861 39,628 3,146,185 29,305 24,689 14,209 7,960 10,754 3,104 3,0208/15/14 42,523 1,388,626 28,222 1,098,709 196,802. 122,655 36,455 34,575 2,906,044 26,079 21,883 12,974 6,924 9,389 2,750 2,6818/16/14 22,964 1,135,855 18,065 855,557 155,576. 89,600 31,660 25,208 2,311,521 13,902 11,944 7,031 3,614 5,046 1,567 1,4258/17/14 15,175 1,057,486 16,631 771,256 147,725. 82,949 35,577 21,402 2,133,026 9,365 8,043 4,836 2,524 3,301 1,085 1,0038/18/14 23,924 1,331,998 19,226 958,855 182,896. 113,128 40,768 29,172 2,676,043 14,934 12,639 7,592 4,078 5,275 1,632 1,5588/19/14 52,759 1,631,085 25,805 1,303,930 216,157. 142,357 38,881 38,982 3,397,197 31,707 26,524 15,623 8,435 11,001 3,450 3,3698/20/14 51,301 1,610,942 41,376 1,219,408 210,686. 145,034 36,068 41,309 3,304,823 30,262 25,511 14,998 7,968 10,347 3,113 3,0718/21/14 50,870 1,566,923 54,025 1,194,964 206,850. 142,458 40,752 41,779 3,247,751 30,483 25,620 15,048 8,271 10,521 3,222 3,1828/22/14 47,213 1,475,739 29,873 1,125,748 199,253. 136,664 39,157 48,595 3,055,029 28,618 23,807 14,173 7,836 9,963 3,003 2,8478/23/14 28,063 1,278,595 24,582 929,475 183,773. 118,540 39,934 39,629 2,614,528 16,626 14,171 8,408 4,384 5,888 1,813 1,7028/24/14 25,918 1,425,951 21,089 997,161 199,273. 135,822 44,470 45,284 2,869,050 15,782 13,549 8,013 4,094 5,877 1,950 1,6008/25/14 57,923 1,735,700 28,813 1,327,611 234,815. 165,504 44,222 65,564 3,602,229 34,671 29,363 17,201 9,153 12,429 3,740 3,6078/26/14 55,318 1,655,671 41,913 1,221,798 243,436. 169,208 41,093 66,739 3,439,858 32,652 27,542 16,389 8,761 11,673 3,587 3,4328/27/14 51,330 1,554,819 37,960 1,166,460 219,730. 149,022 37,800 62,846 3,228,637 30,358 25,690 15,273 8,148 11,027 3,393 3,1178/28/14 52,294 1,617,978 39,073 1,206,320 231,130. 156,740 44,912 67,299 3,363,452 30,810 26,277 15,411 8,193 11,324 3,313 3,1338/29/14 45,011 1,315,275 27,169 1,068,768 197,225. 132,654 33,256 49,504 2,823,851 27,424 22,740 13,507 9,956 7,378 2,934 2,8728/30/14 26,855 1,213,885 21,681 894,304 168,473. 108,309 34,638 37,559 2,478,849 16,124 13,808 8,236 4,396 6,103 1,961 1,6608/31/14 23,337 1,225,250 18,972 881,930 177,577. 114,724 37,027 36,278 2,491,758 14,237 12,446 7,377 3,732 5,471 1,774 1,5059/1/14 68,306 1,702,416 82,112 1,320,100 339,649. 265,579 58,509 59,608 3,827,973 40,981 34,666 20,559 16,565 11,834 5,051 4,2299/2/14 67,079 1,732,077 41,225 1,245,171 374,843. 298,752 56,108 69,001 3,817,177 39,483 33,487 19,361 10,824 16,536 5,023 4,0649/3/14 66,647 1,682,825 46,074 1,215,051 392,355. 319,068 55,217 60,362 3,770,952 39,046 33,169 19,347 10,870 17,257 5,119 4,0149/4/14 62,145 1,589,750 66,866 1,133,141 439,755. 367,796 53,788 57,868 3,708,964 36,968 31,506 18,537 10,482 17,911 4,888 3,7989/5/14 58,475 1,151,941 42,792 1,016,191 289,606. 229,640 59,790 43,792 2,833,752 34,879 28,762 17,124 9,958 16,507 4,666 3,4449/6/14 36,539 1,292,465 31,482 909,860 282,188. 214,537 59,214 43,467 2,833,213 21,774 18,614 10,935 5,948 10,906 3,141 2,1349/7/14 30,705 1,252,923 21,176 872,473 256,187. 188,559 54,538 39,217 2,685,073 18,602 16,038 9,490 5,006 9,457 2,864 1,8149/8/14 70,081 1,684,917 65,024 1,297,431 374,007. 295,022 99,899 58,983 3,875,283 41,662 35,103 20,425 11,863 20,440 6,332 4,3519/9/14 66,591 1,777,236 45,333 1,275,028 374,591. 295,778 106,792 70,749 3,945,507 39,315 33,152 19,582 11,035 19,177 6,126 4,0769/10/14 64,492 1,626,303 26,315 1,198,760 344,936. 269,591 104,508 65,644 3,636,057 38,771 32,972 19,147 10,449 19,248 6,443 4,0499/11/14 61,783 1,582,956 38,779 1,190,851 346,321. 269,469 101,454 64,548 3,594,378 37,531 31,930 18,618 10,245 18,909 6,628 3,9519/12/14 58,622 1,466,449 67,891 1,108,206 329,171. 251,376 90,859 58,493 3,372,445 34,933 29,552 17,447 10,139 17,558 6,206 3,6299/13/14 35,880 1,262,620 34,392 912,995 244,673. 180,081 65,556 45,111 2,745,428 21,229 18,115 10,768 6,207 10,949 3,939 2,1879/14/14 35,305 1,410,325 26,699 989,044 262,554. 193,091 75,517 52,129 3,009,359 21,262 18,362 10,701 5,874 11,103 3,759 2,1609/15/14 69,773 1,727,974 28,576 1,339,060 276,633. 222,597 58,518 62,369 3,715,727 40,397 34,077 19,570 11,407 20,049 7,021 4,3389/16/14 60,935 1,669,464 26,580 1,236,027 303,762. 229,600 77,035 62,969 3,605,437 35,787 30,297 17,582 9,997 16,992 6,011 3,8069/17/14 59,528 1,621,582 34,777 1,198,495 284,142. 212,499 81,216 60,192 3,492,903 33,941 28,843 16,849 9,183 16,027 5,735 3,4979/18/14 54,643 1,569,195 38,129 1,185,832 282,912. 210,639 86,910 56,624 3,430,241 32,584 27,606 16,169 8,879 15,295 5,544 3,5579/19/14 47,393 1,453,147 28,642 1,102,912 265,858. 196,400 99,479 54,944 3,201,382 28,175 23,809 14,058 7,767 13,377 4,875 3,1199/20/14 25,217 1,178,409 18,644 856,908 204,987. 140,948 73,788 42,976 2,516,660 15,395 13,294 7,966 4,271 7,154 2,802 1,5319/21/14 21,785 1,173,239 20,976 842,657 196,590. 134,730 69,023 41,049 2,478,264 13,245 11,502 6,779 3,521 6,110 2,269 1,4059/22/14 58,181 1,679,707 43,282 1,321,118 264,780. 187,260 37,681 57,930 3,591,758 34,295 29,225 17,209 9,440 16,099 5,847 3,7969/23/14 55,727 1,766,609 55,545 1,297,215 243,659. 166,400 37,319 70,352 3,637,099 32,990 28,056 16,576 9,057 15,042 5,568 3,5289/24/14 55,691 1,625,720 32,631 1,218,493 218,166. 142,845 30,992 64,097 3,332,944 32,339 27,523 16,373 9,010 14,514 5,388 3,5549/25/14 55,295 1,625,150 46,270 1,235,504 216,298. 142,456 29,973 61,076 3,356,727 32,499 27,852 16,451 8,978 14,276 5,277 3,3409/26/14 49,496 1,443,145 29,098 1,102,185 192,323. 122,632 27,571 49,683 2,966,637 29,294 25,018 14,743 8,272 12,862 4,856 3,1609/27/14 27,745 1,184,140 19,556 880,793 158,408. 94,144 24,102 36,631 2,397,774 16,333 13,817 8,157 4,661 7,121 2,738 1,7989/28/14 26,001 1,322,981 26,289 951,521 171,377. 101,840 27,299 41,018 2,642,325 15,537 13,211 6,682 7,808 4,261 2,485 1,6649/29/14 61,597 1,713,121 26,785 1,329,561 224,298. 147,684 32,099 61,419 3,534,967 36,209 30,899 18,263 9,877 15,715 5,684 3,8329/30/14 60,997 1,716,522 23,458 1,271,945 224,164. 150,477 30,085 65,846 3,482,497 35,469 29,983 17,625 14,983 9,600 5,424 3,89810/1/14 58,268 1,663,735 35,897 1,247,673 219,931. 146,561 36,156 57,160 3,407,113 34,154 29,055 17,247 9,346 14,339 5,131 3,65810/2/14 56,348 1,620,534 57,539 1,219,003 211,750. 141,772 37,056 52,701 3,340,355 32,691 27,897 16,431 9,193 13,468 4,845 3,38210/3/14 52,151 1,551,022 35,501 1,178,165 204,035. 133,539 36,175 46,487 3,184,924 30,388 25,989 15,375 8,511 12,554 4,557 3,27210/4/14 31,137 1,354,416 14,704 969,869 172,036. 107,236 34,536 38,824 2,691,621 18,729 16,165 9,451 5,051 7,578 3,019 1,93110/5/14 28,815 1,405,491 22,701 995,565 180,946. 110,772 38,334 42,508 2,796,317 17,685 15,299 9,028 7,080 4,793 2,660 1,78810/6/14 66,259 1,672,994 107,756 1,324,397 218,588. 146,101 100,113 57,554 3,627,503 39,432 33,463 19,738 10,937 16,238 6,137 4,22110/7/14 63,045 1,629,315 67,966 1,220,766 217,878. 145,277 88,497 57,348 3,427,047 37,260 31,756 18,481 10,464 14,935 5,885 4,03010/8/14 60,111 1,607,114 35,349 1,216,202 213,982. 143,751 166,438 60,872 3,443,708 35,098 29,944 17,471 9,809 14,110 5,762 3,83310/9/14 59,178 1,680,873 53,474 1,264,401 222,388. 148,878 111,687 55,011 3,536,712 35,310 30,105 17,707 10,048 14,217 6,034 3,77910/10/14 50,647 1,452,950 29,840 1,095,984 191,646. 126,004 148,260 48,502 3,093,186 30,421 25,776 15,037 8,738 11,979 5,254 3,19010/11/14 26,087 1,150,615 22,526 845,787 149,042. 89,717 67,716 34,849 2,360,252 16,137 13,904 8,278 4,631 6,325 2,973 1,68410/12/14 18,912 1,090,751 20,090 776,093 139,485. 80,546 60,950 31,616 2,199,531 11,475 9,886 5,762 3,368 4,527 2,127 1,20910/13/14 31,632 1,415,695 23,824 995,526 177,787. 110,045 85,679 40,499 2,849,055 19,371 16,773 10,117 5,546 7,680 3,553 2,05010/14/14 67,320 1,676,833 82,561 1,319,898 221,192. 145,651 151,537 56,373 3,654,045 39,286 33,431 19,884 11,547 15,363 7,200 4,25910/15/14 61,190 1,671,909 40,765 1,262,726 220,119. 143,862 67,257 56,536 3,463,174 36,458 30,883 18,175 10,492 14,061 6,401 3,89310/16/14 61,334 1,591,464 39,331 1,220,708 208,428. 134,853 53,971 51,525 3,300,280 36,517 31,075 18,193 10,432 14,295 6,490 3,82510/17/14 54,478 1,458,359 28,141 1,139,396 195,784. 123,423 49,168 46,000 3,040,271 32,551 27,547 16,178 9,395 12,567 5,757 3,45910/18/14 29,893 1,248,568 14,095 929,702 160,753. 97,011 45,332 35,323 2,530,784 18,026 15,371 9,114 5,079 6,959 3,234 1,87310/19/14 22,091 1,092,498 15,374 818,385 138,397. 77,811 23,453 25,134 2,191,052 12,993 11,113 6,592 3,620 5,169 2,313 1,34010/20/14 57,603 1,676,535 24,030 1,322,273 214,922. 140,406 29,703 39,556 3,447,425 33,998 29,169 17,417 9,277 13,073 5,776 3,79510/21/14 55,989 1,643,013 44,151 1,233,551 213,198. 143,262 24,222 46,019 3,347,416 32,670 27,855 16,373 8,938 12,225 5,394 3,55310/22/14 52,487 1,599,690 35,959 1,197,398 210,269. 138,457 24,761 51,472 3,258,006 31,227 26,561 15,586 8,836 11,622 5,043 3,23810/23/14 51,773 1,520,044 28,242 1,151,580 201,023. 131,562 22,605 50,678 3,105,734 29,436 25,388 14,904 8,157 11,166 4,878 3,32610/24/14 47,134 1,404,963 31,671 1,083,576 188,052. 119,872 19,614 52,408 2,900,156 27,373 23,323 13,692 7,805 10,404 4,280 2,86810/25/14 23,894 1,133,244 19,585 840,048 147,625. 87,799 14,304 36,810 2,279,415 14,319 12,195 7,237 5,285 4,017 2,343 1,55110/26/14 20,634 1,178,101 19,104 846,470 149,394. 87,178 17,212 33,662 2,331,121 12,567 10,855 6,504 4,706 3,510 2,017 1,26410/27/14 55,341 1,703,511 26,715 1,328,447 217,608. 144,196 24,237 57,712 3,502,426 32,469 28,028 16,755 9,135 12,407 5,295 3,48610/28/14 57,477 1,701,221 34,565 1,249,311 219,432. 147,937 22,895 63,701 3,439,062 33,130 28,511 16,775 9,277 12,529 5,172 3,43910/29/14 53,368 1,679,516 36,139 1,220,318 216,137. 144,976 25,720 67,520 3,390,326 30,237 25,954 15,319 8,612 11,161 4,592 3,05810/30/14 52,520 1,535,314 31,058 1,144,713 202,144. 132,980 21,335 56,031 3,123,575 30,529 26,040 15,499 8,746 11,373 4,598 3,25710/31/14 49,371 1,457,314 29,293 1,112,085 194,414. 126,113 20,502 47,467 2,987,188 28,714. 24,581. 14,444. 8,181. 10,736. 4,361 2,986

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EJEMPLOPRACTICO–Modelodeatribución6.Searmaelmodelo,simplificamos.

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EJEMPLOPRACTICO–Modelodeatribución7.Serepiteelprocesodeaprendizaje,opGmizandoel

modelodeatribución.

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EJEMPLOPRACTICO–Modelodeatribución7.Serepiteelprocesodeaprendizaje,opGmizandoel

modelodeatribución.

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PASAMOSDEUNMODELOHIPPODRIVEN

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AUNMODELODATADRIVEN

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JuanDamia–CEOdeIntellignos@JuanDamia@Intellignos

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